1 slow intelligence systems - a new approach for component-based software engineering

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Slow Intelligence Systems

- A New Approach for Component-based

Software Engineering

演講者：張系國

教授

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Prof. S. K. Chang演講者— 張系國

知識系統學院創辦人 (Founder, Knowledge Systems Institute www.ksi.edu)。

旅美教授，任教於匹茲堡大學（ Professor, University of Pittsburgh www.pitt.edu）。

張教授除了是電腦科學學者外，亦從事小說創作 (Writer and novelist)。

www.cs.pitt.edu/~chang

http://folkartist.e-lib.nctu.edu.tw/collection/chang/intro/intro.html

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IT does not matter!What is the matter?What is “W-H-A-T”?Enabling TechnologiesSlow Intelligence

SystemsSIS ApplicationsQ & A

OOuuttlliinnee

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In 2003, Nicholas Carr wrote an interesting article in Harvard Business Review. Its title is:

“IT Doesn’t Matter”

He argued that information technology is no longer the decisive factor in business. This article caused quite a stir. A lot of IT gurus, including Bill Gates, argued against Carr’s view.

If IT does not matter, WHAT is the matter?

http://hbswk.hbs.edu/item.jhtml?id=3520&t=technology

What is IT?What is IT?

IT=?

Information Technology

If IT does not matter, WHAT is the matter?

What is the matter?

What is the matter?

Let us return to the future.…

What is “What is “W-H-A-W-H-A-TT”?”?

AATTHH

WWTraining

Healthcare

Warfare?

Amusement?

Weisure

Agriculture

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W-H-A-T is in common?

• Connected• Multiple sourced• Knowledge-based• Personalized• Hybrid

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Smarter Planet• We are all now connected - economically,

technically and socially. Our planet is becoming smarter via integration of information scattered in many different data sources: from the sensors, on the web, in our personal devices, in documents and in databases, or hidden within application programs. Often we need to get information from several of these sources to complete a task. Examples include healthcare, science, the business world and our personal lives. (Quoted from Josephine M. Cheng, IBM Fellow and Vice President of IBM Research)

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(courtesy of IBM)

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Hybrid Intelligence• While processor speed and storage capacity

have grown remarkably, the geometric growth in user communities, online computer usage, and the availability of data is in some ways is even more remarkable. Hybrid Intelligence offers great opportunities we have to harness this data availability to build systems of immense potential. While today s large scale systems are evolutionarily based on the distributed computing technologies envisioned in the 70 s and 80 s, sheer scaling has led to many unanticipated challenges. (quoted from Alfred Z. Spector, Vice President, Research and Special Initiatives, Google, USA)

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Hybrid IntelligenceUsers and computers doing more than either could

individually (quoted from Alfred Z. Spector, Google).

Enabling TechnologiesEnabling Technologies

Wireless Communication &

Networking


Mobile Knowledge Agents


Embedded Systems


Distributed Multimedia Systems


Knowledge BasedSoftware Engineering

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Slow Intelligence Systems• Slow Intelligence Systems are general-

purpose systems characterized by being able to improve performance over time.

• A slow intelligence system is a system that (i) solves

problems by trying different solutions, (ii) is context-

aware to adapt to different situations and to propagate

knowledge, and (iii) may not perform well in the

short run but continuously learns to improve its

performance over time.

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purpose systems characterized by being able to improve performance over time

through a process involving • Enumeration

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through a process involving • Enumeration• Propagation

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through a process involving • Enumeration• Propagation• Adaptation

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through a process involving • Enumeration• Propagation• Adaptation• Elimination

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• Concentration

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• Concentration• Slow Decision Cycle to complement Fast

Decision Cycle

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Slow Intelligence Systems

• A SIS continuously learns, searches for new solutions and propagates and

shares its experience with other peers.

• From the structural point of view, a SIS is a system with multiple decision

cycles such that actions of slow decision cycle(s) may override actions of quick decision cycle(s), resulting in

poorer performance in the short run but better performance in the long-

run.

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SIS Basic Building Block (BBB)

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Mathematical Formulation of BBB• For the two-decision-cycle SIS, the

basic building block BBB can be formulated methematically as:

if timing-control(t) == 'slow' then y(t)solution = gconcentrate (geliminate (gadapt (genumerate (x(t) problem))))

else if timing-control(t) == 'quick' then y(t) solution = fconcentrate (feliminate (fadapt (fenumerate (x(t) problem))))

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Advanced Building Block (ABB)

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SIS built from BBBs and ABBs

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OUR RESEARCH AGENDAA Framework to study Natural Slow Intelligence

Systems

A Test bed to develop Artificial Slow Intelligence Systems

Component based

Multiple decision cycles

Evolutionary ontology

Learning rules

Visualization

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The SIS Testbed

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Production of personalized or custom-tailored goods or services to meet consumers' diverse and changing needs

“Like its driver each Toyota Echo is unique!”

SIS Application to Product Configuration

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System Architecture

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Ontological Transformations

User Layer

Functionality Layer

Components Layer

Instance

Layer

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Sequence of Ontological Transformations

In this way, the configuration problem CP can be formulated in its general

formulation as the composition of ontological transformations:

FC(FEL(FA(FEN(UR, UP)))).

Similar to a SIS, the proposed Configurator can follow a slow and a fast

process of solution inference. So, the previous formulation can be defined as

the slow process, while the fast process can be defined as a simplified sequence of ontological transformations: FC(FEN(UR,

UP)).

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A Scenario

• A customer would like to buy a Personal Computer in order to play videogames and surf on the internet.

• He knows that he needs an operating system, a web browser and an antivirus package.

• In particular, the user prefers a Microsoft Windows operating system. He lives in the United States and prefers to have a desktop. He also prefers low cost components.

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Ontology for Product Configurator

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Experimental Results

• A set of common computer configurations based on usage scenarios were identified for evaluation

• The allowed configurations for the personal computer are so named:

• Play_Videogame (PV)• Web_Surfing (WS)• Online_Gaming (OG)• Multimedia_Design (MD)• Computer_Aided_Design (CAD)• Music (MUS)• Word_Processing (WP)• School_Work - Web_Surfing and Word_Processing

(SW)

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User Satisfaction Index after 50 similar requests

StoreRequest

FEI

EEI OSI

OEI (obtained after

50 request

s)

USI (avg. value

obtained after 50 requests)

Italian PV 1.00

0.94

0.85

0.14 0.87

American

WP 1.00

0.86

0.83

0.11 0.88

British CAD 0.96

0.88

0.79

0.17 0.83

Indian MD 1.00

0.90

0.93

0.07 0.97

Japanese

WS 1.00

0.92

0.91

0.06 0.98

OSI Evolution Index

0

0,2

0,4

0,6

0,8

1

1,2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Id_Request

OS

I

Italian

American

British

Indian

Japanese

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SIS Application to Detect Trends/Topics

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SIS module 1

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SIS module 2

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Discussion• There are a large number of

intelligent systems, quasi-intelligent systems and semi-intelligent systems that are "slow". Distributed intelligence systems, multiple agents systems and emergency management systems are mostly slow intelligence systems that exhibit the characteristics of multiple decision cycles.

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Discussion (continued)

• Since time is relative, "slow" intelligence systems for some can also be "fast" for others.

• A slow intelligence system can evolve

into a fast intelligence system.

• A SIS differs from expert systems in that the learning is not always obvious.

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Conclusions

• In the age of micro-profit economy, Information Technology to acquire, communicate and apply knowledge to reduce cost and improve efficiency will still be a decisive factor.

• IT is KNOWLEDGE TECHNOLOGY.• EVERY INDUSTRY is IT INDUSTRY.

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What is the matter?The future is already here!

The End

1 slow intelligence systems - a new approach for component-based software engineering

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